docstr: Return:
      A list of predictions.
      i-th element represents prediction at i-th position of the sequence.
docstr_tok: ["Return",":","A","list","of","predictions",".","i-th","element","represents","prediction","at","i-th","position","of","the","sequence","."]
code: def forward(self, batched_data):
      """
      Return:
          A list of predictions.
          i-th element represents prediction at i-th position of the sequence.
      """
  
      h_node = self.gnn_node(batched_data)
  
      h_graph = self.pool(h_node, batched_data.batch)
  
      pred_list = []
  
      for i in range(self.max_seq_len):
          pred_list.append(self.graph_pred_linear_list[i](h_graph))
  
      return pred_list
  
file_path: ogb/examples/graphproppred/code2/gnn.py
imports: import torch
  from torch_geometric.nn import MessagePassing
  from torch_geometric.nn import global_add_pool, global_mean_pool, global_max_pool, GlobalAttention, Set2Set
  import torch.nn.functional
  from torch_geometric.nn.inits import uniform
  from conv import GNN_node, GNN_node_Virtualnode
label_desc: Use image node_id 2 for calling the GNN obj's underlying member method code with example usage: obj.forward(batched_data) and returns: pred_list